The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services

The operation of a demand responsive transport service usually involves the management of dynamic requests. The underlying algorithms are mainly adaptations of procedures carefully designed to solve static versions of the problem, in which all the requests are known in advance. However there is no guarantee that the effectiveness of an algorithm stays unchanged when it is manipulated to work in a dynamic environment. On the other hand, the way the input is revealed to the algorithm has a decisive role on the schedule quality. We analyze three characteristics of the information flow (percentage of real-time requests, interval between call-in and requested pickup time and length of the computational cycle time), assessing their influence on the effectiveness of the scheduling proces

Effective Heuristics to Solve Pickup and Delivery Problems with Time Windows

Pickup and delivery problem with time windows (PDP-TW) is a challenging scheduling problem for which each delivery is coupled with a pickup request. Metaheuristic search techniques like the tabu search have been used to solve PDP-TW. In this paper, we investigated a min-conflicts based micro-genetic algorithm combining some interesting construction heuristic, namely the Align-Fold or Boomerang, and repair heuristics including a new Swap operator and a modified billiard operator to effectively solve PDD-TW. Our results compared favorably against those of a tabu-embedded metaheuristic search on a set of modified Solomon's test cases. More importantly, our proposed heuristics can easily be integrated into many search schemes for solving other complex scheduling problems.published_or_final_versio

Scheduling Deliveries with Backhauls in Thailand's Cement Industry

In this study, the Truckload Delivery with Backhaul Scheduling Problem (TDBSP) is formulated and an Ant Colony Optimization methodology developed for a related problem, the Vehicle Routing Problem with Backhaul and Time Windows (VRPBTW), is adapted for its solution. The TDBSP differs from the VRPBTW in that shipments are in units of truckloads, multiple time windows in multiple days are available for delivery to customers, limited space for servicing customers is available and multiple visits to each customer may be required. The problem is motivated by a real-world application arising at a leading cement producer in Thailand. Experts at the cement production plant assign vehicles to cement customers and lignite mines based on manual computations and experience. This study provides mathematical and computational frameworks for modeling and solving this real-world application

Heuristic algorithms for a vehicle routing problem with simultaneous delivery and pickup and time windows in home health care

International audienceThis paper addresses a vehicle scheduling problem encountered in home health care logistics. It concerns the delivery of drugs and medical devices from the home care company's pharmacy to patients' homes, delivery of special drugs from a hospital to patients, pickup of bio samples and unused drugs and medical devices from patients. The problem can be considered as a special vehicle routing problem with simultaneous delivery and pickup and time windows, with four types of demands: delivery from depot to patient, delivery from a hospital to patient, pickup from a patient to depot and pickup from a patient to a medical lab. Each patient is visited by one vehicle and each vehicle visits each node at most once. Patients are associated with time windows and vehicles with capacity. Two mixed-integer programming models are proposed. We then propose a Genetic Algorithm (GA) and a Tabu Search (TS) method. The GA is based on a permutation chromosome, a split procedure and local search. The TS is based on route assignment attributes of patients, an augmented cost function, route re-optimization, and attribute-based aspiration levels. These approaches are tested on test instances derived from existing VRPTW benchmarks

An Adaptive Tabu Search Heuristic for the Location Routing Pickup and Delivery Problem with Time Windows with a Theater Distribution Application

The time constrained pickup and delivery problem (PDPTW) is a problem of finding a set of routes for a fleet of vehicles in order to satisfy a set of transportation requests. Each request represents a user-specified pickup and delivery location. The PDPTW may be used to model many problems in logistics and public transportation. The location routing problem (LRP) is an extension of the vehicle routing problem where the solution identifies the optimal location of the depots and provides the vehicle schedules and distribution routes. This dissertation seeks to blend the PDPTW and LRP areas of research and formulate a location scheduling pickup and delivery problem with time windows (LPDPTW) in order to model the theater distribution problem and find excellent solutions. This research utilizes advanced tabu search techniques, including reactive tabu search and group theory applications, to develop a heuristic procedure for solving the LPDPTW. Tabu search is a metaheuristic that performs an intelligent search of the solution space. Group theory provides the structural foundation that supports the efficient search of the neighborhoods and movement through the solution space